Can Including Cryptocurrencies with Stocks in Portfolios Enhance Returns in Small Economies? An Analysis of Fiji’s Stock Market
Bibliographic record
Abstract
The market for digital assets, and more specifically cryptocurrencies, is growing, although their adoption in small island countries remains absent. This paper explores the potential benefits of integrating cryptocurrencies into portfolios alongside stocks, with a focus on Fiji’s stock market. This is the first study on a small market like Fiji, which emphasizes the role of cryptocurrencies in portfolio management. We analyze the outcomes (returns and risks) of combining cryptocurrencies with stocks using 12 different techniques. We use monthly stock returns data of 18 companies listed on the South Pacific Stock Exchange from Aug-2019 to Jun-2025 (71 months) and nine cryptocurrencies from Sept-2019 to Jun-2025 (70 months). Our main analysis shows that only one cryptocurrency, albeit with a small exposure, consistently appears in the stock-cryptocurrency portfolios in the 12 methods. Using the return-to-risk ratio across methods as a guide, we find that the stocks-cryptocurrencies portfolio based on EQW, MinVar, MaxSharpe, MinSemVar, MaxDiv, MaxDeCorr, MaxRMD, and MaxASR offers better outcomes than the stock-only portfolios. Using high returns as a guide, we find that six out of 12 methods (EQW, MaxSharpe, MaxSort, MaxCEQ, MaxOmega, and MaxUDVol) support the stocks-cryptocurrencies portfolios. Portfolios satisfying both conditions (high return-risk ratio and high return) are supported by the EQW and MaxSharpe portfolios. The consistency of assets in both stock and stock−cryptocurrency portfolios is further confirmed by 24-month out-of-sample forecasts and Monte Carlo simulations, although the latter supports small exposures in two out of the nine cryptocurrencies. Based on the results, we conclude that a small exposure to certain cryptocurrencies can strengthen diversification and improve potential returns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".